Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?
Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference (2025)
Abstract
The paper presents an overview of the fourth edition of the Shared Task on Multilingual Coreference Resolution, organized as part of the CODI-CRAC 2025 workshop. As in the previous editions, participants were challenged to develop systems that identify mentions and cluster them according to identity coreference. A key innovation of this year’s task was the introduction of a dedicated Large Language Model (LLM) track, featuring a simplified plaintext format designed to be more suitable for LLMs than the original CoNLL-U representation. The task also expanded its coverage with three new datasets in two additional languages, using version 1.3 of CorefUD – a harmonized multilingual collection of 22 datasets in 17 languages. In total, nine systems participated, including four LLM-based approaches (two fine-tuned and two using few-shot adaptation). While traditional systems still kept the lead, LLMs showed clear potential, suggesting they may soon challenge established approaches in future editions.
Authors
BibTex
@inproceedings{novak-etal-2025-findings, title = "Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can {LLM}s Dethrone Traditional Approaches?", author = "Nov{\'a}k, Michal and Konopik, Miloslav and Nedoluzhko, Anna and Popel, Martin and Prazak, Ondrej and Sido, Jakub and Straka, Milan and {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k and Zeman, Daniel", editor = "Ogrodniczuk, Maciej and Novak, Michal and Poesio, Massimo and Pradhan, Sameer and Ng, Vincent", booktitle = "Proceedings of the Eighth Workshop on Computational Models of Reference, Anaphora and Coreference", month = nov, year = "2025", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.crac-1.9/", pages = "95--118", abstract = "The paper presents an overview of the fourth edition of the Shared Task on Multilingual Coreference Resolution, organized as part of the CODI-CRAC 2025 workshop. As in the previous editions, participants were challenged to develop systems that identify mentions and cluster them according to identity coreference. A key innovation of this year{'}s task was the introduction of a dedicated Large Language Model (LLM) track, featuring a simplified plaintext format designed to be more suitable for LLMs than the original CoNLL-U representation. The task also expanded its coverage with three new datasets in two additional languages, using version 1.3 of CorefUD {--} a harmonized multilingual collection of 22 datasets in 17 languages. In total, nine systems participated, including four LLM-based approaches (two fine-tuned and two using few-shot adaptation). While traditional systems still kept the lead, LLMs showed clear potential, suggesting they may soon challenge established approaches in future editions." }
